Enerkem (Canada)

BackgroundFalls remain a leading cause of injury among individuals with cognitive changes, necessitating reliable detection and response systems. Ambient sensing technology provides 24/7 fall detection capabilities and customizable escalation protocols, aiming to minimize unnecessary calls to emergency dispatch services (911). This study explored the feasibility of implementing escalation processes to manage falls effectively within home care settings.MethodA feasibility study was conducted with 25 participants across 23 homes that had care for at least one month. Client care managers (N = 8) provided feedback from five cities: Montreal, Toronto, Winnipeg, Calgary, and Vancouver. Two modes of ambient sensing technology were utilized: one focused on falls and the other on broader monitoring capabilities, including wandering and inactivity detection. Both systems sent notifications to an alert center.The alert center's process included:1. Identifying the location and duration of the fall.2. Checking the home to determine if the individual was alone.3. Escalating to the “circle of friends,” a pre‐identified contact group, via phone or SMS.4. If no response was received, the system could escalate to a 911 wellness check or client care manager, based on the families’ preferences.Qualitative feedback was collected from families and care team staff to assess preferences and barriers to implementing the fall detection protocols.ResultNearly all families (24 out of 25) indicated during assessment that they preferred escalation to the “circle of friends” rather than an automatic 911 dispatch, highlighting that emergency response is not always the desired solution. Participants appreciated the flexibility and customization of the escalation process. The inactivity alert system, which learns daily habits, successfully minimized false alarms by sending SMS notifications to confirm well‐being before escalating.ConclusionAmbient sensing technology for fall detection is a feasible and acceptable solution when supported by tailored escalation protocols. The inclusion of a “circle of friends” approach ensures a personalized response, reducing reliance on 911 and addressing concerns about unnecessary interventions. Future research will further refine these protocols and validate their effectiveness in diverse care settings.

Recent studies highlight coarse-grained discrete element method (CGDEM) as a helpful tool for mitigating the computational cost associated with original discrete element method (DEM) simulations in large-scale industrial applications such as fluidized beds. This approach achieves cost savings by reducing the number of particles in the domain, while increasing their size. In the current work, CGDEM-LES (large-eddy simulation) numerical simulations are conducted on a 3D (three-dimensional) cylindrical pilot-scale fluidized bed in the bubbling regime, containing 9.6 M Geldart B-type particles. Macroscopic and mesoscopic analyses are performed, revealing qualitative and quantitative effects of coarse-graining on bed behavior. Among these effects, a global homogenization of the fluidized region is observed, marked by soaring bed surfaces, lower solid velocity, and solid fraction gradients. Additionally, a decrease in the bubble population is reported. These effects intensify as the coarse-graining factor increases. Despite influencing some results, the impact of mesh size is deemed negligible compared to that of particle coarse-graining. Some of the observed issues can be alleviated by incorporating additional mechanisms from the literature, aiming to dissipate the extra energy inherently present in coarse-grained systems. However, these mechanisms prove to be barely sufficient to replicate DEM results with the smallest coarse-graining factor tested. A thorough analysis allows identifying a side effect of one of these approaches, which is to slow particles down all the more as they move fast, causing a macroscopic misprediction of particle vertical velocity in turn. This model is then deemed less useful in the context of this study.

Recent studies point CGDEM (Coarse-Grained Discrete Element Method) as a valuable tool to circumvent the cost of original DEM (Discrete Element Method) simulations for large-scale industrial applications such as fluidized beds. In this approach, cost savings are ensured by decreasing the number of particles in the domain, while increasing their size. In the present work, CGDEM-LES (Large-Eddy Simulation) numerical simulations are carried out on a 3D cylindrical pilot-scale fluidized bed in the bubbling regime and gathering 9.6M Geldart B-type particles. A macroscopic analysis is performed and allow observing the effects of coarse-graining on the bed behavior qualitatively and quantitatively. Among them, a global homogeneization of the fluidized region, characterized by higher bed surfaces, lower solid velocity and solid fraction gradients, is reported, along with a drop in the bubble population. These effects are observed to intensify as the coarse-graining factor increases. Some of the reported issues can be alleviated by employing additional mechanisms from the literature, aiming at dissipating the extra amount of energy inherently present in coarse-grained systems. However, these are barely sufficient to retrieve DEM results with the smallest coarsegraining factor tested.

Recently, attention has been drawn to the CGDEM (coarse-grained discrete element method) as a valuable option to circumvent the cost of classic DEM (discrete element method) computations for large-scale industrial applications such as fluidized beds. It consists of increasing the particle size while decreasing their number, hence the benefit in the cost of the simulation. Various coarse-graining approaches have been reported in the literature, but only a few authors have suggested mechanisms to overcome the reduction of the collision frequency inherent to the coarse graining process. This study proposes a comparison between three solutions from the literature to this problem. Coarse grained numerical simulations are carried out on an elementary HCS (homogeneous cooling system) test case and confirm the existence of an inverse law for the drop in the collision frequency. If not compensated, missed contacts lead to an underprediction of the expected granular temperature decay rate, which can be quantitatively recovered using one of these approaches. As regular DEM simulations, the CGDEM also exhibits a propensity for the onset of instabilities, which are further discussed in the second part of this study. A dependency of the critical domain length associated with the onset of velocity vortices in HCS with respect to the coarse graining factor is predicted. It indicates that coarse grained simulations might be more stable than their DEM counterpart. This is qualitatively assessed by visualizing a locally averaged particle velocity field. A quantitative method based on the computation of the local granular temperature distribution allows validating these observations in most cases, by exhibiting a global shift toward lower variances. Repetitions are performed to estimate a characteristic time to instability, which is seen to be shorter for coarse grained simulations, although these show smaller discrepancies with Haff's law over longer times.

Abstract A hybrid Euleran-Lagrangian Dense Discrete Particle Model (DDPM) was used to numerically simulate the bubbling behavior of a fluidized bed reactor. The model exploits the parcels concept to reduce the number of particles to simulate while exploiting the Kinetic Theory of Granular Flow (KTGF) to account for their repulsive interactions. The DDPM-KTGF was explored throughout a model sensitivity analysis to identify the most influent parameters impacting on the numerical accuracy and performances to ultimately assess its potential use for industrial purposes. Because of the measurement simplicity as well as its strong connection with the bed fluid-dynamic, pressure-drop data was used and processed to obtain the power spectral density (PSD) distribution to empirically and numerically characterize the behavior of this system under a bubbling fluidization regime. The DDPM-KTGF model was found to be sensitive to mesh size, restitution coefficients but mostly to the drag law. However, poor sensitivity to the kinetic viscosity, solid pressure, radial distribution function as well as to the number of parcels was revealed. Besides having an effect on the physical outputs, the mesh refinement was also required to numerically verify the model which also had a significant impact on the simulation time-performance. Moreover, a major barrier was found when using this model to simulate fixed bed regime, showing the limitation of the KTGF approach to high particle density regions as a result of a poor estimation of particles force interactions.

Recycling carbon present in residual streams enhances sustainability and creates local wealth.Enerkem Inc. is a leading biomass gasification company headquartered in Montreal, Québec.The approach Enerkem has been developing involves: identification of low cost residual streams as feedstock, sorting, biotreatment (anaerobic and/or aerobic) and preparation of an ultimate residue (RDF).The latter is a rather uniform material that can be fed, as a fluff, to a bubbling bed gasifier in a staged gasification to carry out, sequentially, the needed chemical reactions that result in high syngas yields.Process can be adjusted to reach desired gas composition for synthesis or electricity generation as well as gas conditioning to produce an ultraclean syngas.Products for such a process are: i) syngas with an appropriate range of H 2 /CO ratios, ii) CO 2 (which is recovered), iii) solid char as a residue composed of the inorganic fraction of the raw material and some unconverted carbon that "coats" the inorganic matrices, and iv) water that needs to be treated to meet the sewage specifications and thus be sent into the water distribution system of a given municipality.Enerkem is developing two parallel valorisation routes; a) heat and power and b) synthesis of (bio)methanol as a high yield product.The methanol is the platform intermediate that can be turned into ethanol (also with high yields), and other green chemicals.Yields of ethanol as the end product are above 350Energy and Sustainability III 123 www.witpress.com